Abstract
This chapter discusses discriminant analysis, a statistical method for handling classification problem, and applies the analysis to predict sovereign debt crisis by differentiating two groups, “Default” and “Non-default”, based on certain quantitative and qualitative country characteristics. The model is tested on a new country to determine which of the two groups it belongs, and the model correctly predicts default. With the same characteristics for the discriminant function, the logit function, which measures the odds of default in relation to such characteristics, is also estimated. For classification purposes, discriminant analysis uses normal distribution, whereas the logit model assumes a distribution with fatter tails compared to normal distribution, thus making logit analysis more relevant in the presence of abnormal and extreme values in the population.
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Notes
- 1.
The yield spread between the Emerging Market Local Credit Index and the US Generic Government 5 Year Index was more than 5.50% during the 2008 financial crisis. The spread has narrowed after the recovery to around 2.85% in July 2018.
- 2.
Refer to Moody’s Investors Services, Sovereign Default and Recovery Rates, 1983–2016, 30 June 2017; and Moody’s Investors Service, Sovereign Defaults and Restructurings, October 2013.
- 3.
Nagy, Pancras. J, (1984), Country Risk: How to Assess, Quantify, and Monitor It (London, Euromoney Publications, 1984). Country risk analysis and forecasting country default are a relatively new area of study that grew out of commercial banks’ recycling of the current account surpluses of OPEC countries in the 1970s. See P. J. Nagy (1984), ibid.; M. H. Bouchet, Ephraim Clark and Bertrand Groslambert (2002), Country Risk Assessment, A guide to Global Investment Strategy, Wiley Finance, 2002. In this textbook, you find a comprehensive survey of the qualitative and quantitative method to country risk analysis.
- 4.
There is no concise definition of a complex system, so it is more instructive to list the essential features of the system. A complex system includes many everyday examples such as a truck, a sports team, banking, an economy, and the human body. The common thread is that they all have goals, which drive their behaviour. A complex system comprises many individual pieces but it is not so much the number of individual pieces that is important for complexity but how they connect with each other. Thus, linkages are a necessary feature of all complex systems. Complex systems exhibit non-linearity (changes in input lead to disproportionate changes in output), feedback loops, and adaptiveness in their behaviour.
- 5.
Standard and Poor’s (2017), 2016 Annual Sovereign Default Study and Rating Transitions, April 2017.
- 6.
Euromoney, Currency Risk Methodology. https://www.euromoney.com
- 7.
The PRS Group Inc., International Country Risk Guide Methodology.
- 8.
Standard and Poor’s (2017), Sovereign Rating Methodology, December 18, 2017.
- 9.
Moody’s Investors’ Service (2016), Ratings Methodology – Sovereign Bond Ratings, December 22, 2016.
- 10.
A strictly pre-emptive (sovereign) debt restructuring occurs when default risk is high ex ante and restructuring takes place pre-emptively without missing any payments to creditors. In practice, pre-emptive debt restructurings involve some missed payments but only temporarily. In contrast, in post-default debt restructuring, the borrower defaults unilaterally before it starts to renegotiate its debt. Most debt rescheduling are pre-emptive because the cost to the country due to a cessation of foreign credit is high in terms of lower real GDP. Various IMF studies suggest “severe credit and net capital inflow declines occur more likely following post-default restructurings than weakly or strictly pre-emptive restructurings.” See IMF (2019), Costs of Sovereign Defaults: Restructuring Strategies by Tamon Asonuma, Marcos Chamon, Aitor Erce, and Akira Sasahara, WP/19/69. Bank Distress and the Capital Inflow-Credit Channel.
- 11.
See these authors: W. W. Cooley, and P. R. Lohnes (1971), Multivariate Data Analysis, New York, John Wiley & Sons. B. Efron (1975), The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis, Journal of the American Statistical Association, 70, 892–898. D. J. Hand (1981), Discrimination and Classification, New York, John Wiley & Sons; and D. J. Hand (1982), Kernel Discriminant Analysis, New York: Research Studies Press.
- 12.
Altman, E. I. (1968), Financial Ratios, Discriminant Analysis, and the Prediction of Corporate Bankruptcy, Journal of Finance 22 (September), pp. 589–609.
- 13.
Klecka, W. R. (1980), Discriminant Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, 07–019, Beverly Hills and London: Sage Publications.
- 14.
Morrison, D. F., (2005), Multivariate Statistical Methods, Thomson/Brooks/Cole.
- 15.
Greene, W. H., (2012), Econometric Analysis, 7th edition, Prentice Hall.
- 16.
Carey, G. (1998), Multivariate Analysis of Variance (MANOVA): I. Theory, pdf file.
- 17.
Altman, E. I., (1968). Financial Ratios, Discriminant Analysis, and the Prediction of Corporate Bankruptcy, Journal of Finance 22 (September), pp. 589–609. Edward I. Altman (1993) Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy, Wiley.
- 18.
Edwards, B., (2004), Credit Management Handbook, fifth edition, Gower.
- 19.
Bernstein, L. A., and John J. Wild (1993), ibid.
- 20.
G. S. Maddala, ibid.
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Appendix 9.1: Countries and Debt Crisis Episodes
Appendix 9.1: Countries and Debt Crisis Episodes
Country | External debt crisis episodes |
|---|---|
Argentina | 1982 |
Belize | No debt crisis |
Bolivia | 1980–1981 |
Botswana | No debt crisis |
Brazil | 1983 |
Cameroon | 1985 |
China | No debt crisis |
Colombia | 1982, 1983 |
Costa Rica | 1981, 1984 |
Dominican Republic | 1982, 1983 |
Ecuador | 1982, 1983 |
El Salvador | 1981 |
Ethiopia | No debt crisis |
Gabon | No debt crisis |
Ghana | 1981, 1982 |
Guyana | 1982 |
India | No debt crisis |
Indonesia | No debt crisis |
Jamaica | 1981 |
Jordan | No debt crisis |
Kenya | No debt crisis |
Malaysia | No debt crisis |
Lesotho | No debt crisis |
Mexico | 1982 |
Morocco | 1983 |
Nepal | No debt crisis |
Nicaragua | 1979 |
Nigeria | 1983 |
Panama | 1983 |
Peru | 1984 |
Pakistan | No debt crisis |
Philippines | 1983, 1984 |
Senegal | 1981 |
Sierra Leone | 1980 |
Sri Lanka | No debt crisis |
Sudan | 1981 |
Thailand | No debt crisis |
Tunisia | No debt crisis |
Turkey | 1980, 1981 |
Venezuela | 1983 |
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Yhip, T.M., Alagheband, B.M.D. (2020). Statistical Methods of Predicting Country Debt Crisis. In: The Practice of Lending. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-32197-0_9
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